Competition, Markups, and the Gains from International Trade: Appendix, Not for Publication Chris Edmond⇤

Virgiliu Midrigan†

Daniel Yi Xu‡

June 2014

Contents A Data

2

A.1 Data description and product classification . . . . . . . . . . . . . . . . . . . . . . .

2

A.2 Firm-level moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

A.3 Markup estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

A.4 Nonparametric productivity distribution . . . . . . . . . . . . . . . . . . . . . . . . .

5

B Robustness experiments and sensitivity analysis B.1 Correlated

xi (s), x⇤i (s)

7

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

B.2 Labor wedges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

B.3 Heterogeneous tari↵s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8

B.4 Bertrand competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

B.5 Sensitivity to

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

B.6 No fixed costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

B.7 Gaussian copula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

B.8 Uncorrelated

n(s), n⇤ (s)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

B.9 5-digit sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

B.10 Fixed N competitors per sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

C Extensions

12

C.1 Capital accumulation and elastic labor supply . . . . . . . . . . . . . . . . . . . . . .

12

C.2 Asymmetric countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

C.3 Free entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

⇤

University of Melbourne, [email protected]. New York University and NBER, [email protected]. ‡ Duke University and NBER, [email protected]. †

1

This appendix is organized as follows. In Appendix A we provide more details on our data, our procedure of inferring markups, and alternative methods for inferring producer-level productivity. In Appendix B we provide further details of the robustness experiments mentioned in the main text as well as related sensitivity analysis. In Appendix C we provide further details on three more substantial extensions of our benchmark model, namely: (i) a dynamic model with endogenous capital accumulation and labor supply, (ii) asymmetric countries that di↵er in size and/or economywide productivity, and (iii) a free-entry model with an endogenous number of competitors per sector.

A A.1

Data Data description and product classification

We use the Taiwan Annual Manufacturing Survey. Our sample covers the years 2000 and 2002–2004. The year 2001 is missing because in that year a separate census was conducted. The dataset we use has two components. First, an establishment-level component collects detailed information on operations, such as employment, expenditure on labor, materials and energy, and total revenue. Second, a product-level component reports information on revenues for each of the products produced at a given establishment. Each product is categorized into a 7-digit Standard Industrial Classification created by the Taiwanese Statistical Bureau. This classification at 7 digits is comparable to the detailed 5-digit SIC product definition collected for US manufacturing establishments as described by Bernard, Redding and Schott (2010). Panel A of Table A1 gives an example of this classification while Panel B reports the distribution of 7-digit sectors within 4- and 2-digit industries. Most of the products are concentrated in the Chemical Materials, Industrial Machinery, Computer/Electronics and Electrical Machinery industries.

A.2

Firm-level moments

The Taiwanese manufacturing sector is dominated by single-establishment (single-plant) firms. In our data, 98% of firms are single-plant firms and these firms account for 92% of total manufacturing sales. Consequently, whether we choose firms or plants as our unit of analysis makes little di↵erence for our analysis. As reported in Table A2, our key micro and sectoral concentration moments are very similar whether we use firms or plants. We use plant-level data for our benchmark model because it is the natural unit of analysis at which to measure a producer’s production technology.

A.3

Markup estimation

In our model, as is standard in the trade literature, labor is the only factor of production and a producer’s revenue productivity is its markup. But in comparing our model’s implications for markups to the data, it is important to recognize that, in general, revenue productivity di↵ers across producers not only because of markup di↵erences but also because of di↵erences in the technology with which they operate. To control for this heterogeneity, we use modern IO methods to purge our

2

markup estimates of the di↵erences in technology that surely exist across Taiwanese manufacturing industries. Controlling for heterogeneity in producer technology.

To map our model into micro-level

production data, we relax the assumptions of a single factor of production and constant returns to scale. In particular, we follow De Loecker and Warzynski (2012) and assume a translog gross production function log yi = ↵l log li + ↵k log ki + ↵m log mi + ↵ll (log li )2 + ↵kk (log ki )2 + ↵mm (log mi )2 + ↵lk (log li log ki ) + ↵lm (log li log mi ) + ↵km (log ki log mi ) + log ai , where li denotes labor, ki denotes physical capital, mi denotes material inputs and ai is physical productivity. The translog specification serves as an approximation to any twice continuously di↵erentiable production function in these inputs and allows the elasticity of output with respect to any variable input, say labor, to di↵er across firms within the same sector. We estimate this translog specification for each 2-digit Taiwanese industry, giving us industryspecific coefficient estimates. Let el,i denote the elasticity of output with respect to labor el,i :=

@ log yi = ↵l + 2↵ll log li + ↵lk log ki + ↵lm log mi . @ log li

(1)

Cost minimization then implies that producer i sets el,i W li = . pi y i µi

(2)

Thus variation in labor input cost shares across producers may be due to either variation in markups µi or to variation in output elasticities el,i . We use data on labor input cost shares and production function estimates of el,i to back out markups µi from (2). Controlling for simultaneity.

As is well-known, a key difficulty in estimating production func-

tions is that input choices li , ki , mi will generally be correlated with true productivity ai . We follow De Loecker and Warzynski (2012) and apply ‘control’ or ‘proxy function’ methods inspired by Olley and Pakes (1996), Levinsohn and Petrin (2003) and Ackerberg, Caves and Frazer (2006) to deal with this simultaneity. More specifically, we write the measurement equation for the translog production function as d log yit = ↵l log lit + ↵k log kit + ↵m log mit + ↵ll (log lit )2 + ↵kk (log kit )2 + ↵mm (log mit )2

+ ↵lk (log lit log kit ) + ↵lm (log lit log mit ) + ↵km (log kit log mit ) + log ait + ✏˜it , d is output in the data and where ✏ where yit ˜it is IID noise.

Our approach to estimating the production function closely follows the procedure in Ackerberg, Caves and Frazer (2006) (and in particular we follow their timing assumptions that rationalize a mapping from a firm’s capital kit , labor lit and productivity ait to its demand for materials). To be specific, we: 3

1. Write the so-called control function as mit = f (kit , lit , ait ) , where, as is standard in the literature, we assume that this function can be inverted to uniquely determine a level of productivity associated with a given configuration of observed inputs, so that we can write log ait = g(kit , lit , mit ) . We can then write the conditional mean of measured log output as h(kit , lit , mit ) = ↵l log lit + ↵k log kit + ↵m log mit + ↵ll (log lit )2 + ↵kk (log kit )2 + ↵mm (log mit )2 + ↵lk (log lit log kit ) + ↵lm (log lit log mit ) + ↵km (log kit log mit ) + g(kit , lit , mit ) , so that log output in the data is simply d log yit = h(kit , lit , mit ) + ✏˜it ,

and we can estimate the conditional mean function h(·) by high-order polynomials. Given the nonparametric function g(·) on the right-hand-side of the conditional mean, no structural parameters of production function can be identified at this stage. The purpose of this representation is to isolate the measurement/transitory shock component ✏˜it which is orthogonal to all inputs at time t. 2. Let ↵ := (↵l , ↵k , ↵m , ↵ll , ↵kk , ↵mm , ↵lk , ↵lm , ↵km ) denote the parameters of the production function and let b hit (↵) := b h(kit , lit , mit , ↵) denote the fitted values for some candidate parameter vector ↵. This implies an estimate of log productivity

b ait (↵) = b hit (↵)

↵l log lit

↵k log kit

↵m log mit

↵lk (log lit log kit )

↵ll (log lit )2

↵lm (log lit log mit )

↵kk (log kit )2

↵mm (log mit )2

↵km (log kit log mit ) ,

Estimating the parameters ↵ then depends on specific parametric assumptions about the data generating process for ait and in particular on how it evolves over time. As in standard literature, we assume that log productivity follows a flexible AR(1) process b ait (↵) = (b ait

1 (↵))

+ ⇣ita (↵) ,

where (·) is a second-order polynomial.

3. Use GMM to estimate the parameter vector ↵. As in the dynamic panel literature, we exploit the sequential exogeneity condition that ⇣ita (↵) is uncorrelated with a vector of lagged input variables, specifically h zit := log lit

1,

log kit , log mit 2

1,

, (log kit )2 , (log mit

(log lit

1)

(log lit

1 log kit ) ,

(log lit 4

1)

2

,

1 log mit 1 ) ,

(log kit log mit

1)

i

.

Note that, as is standard in this literature, capital enters without a lag since it is assumed to be pre-determined. b in hand, we then use data on inputs With estimates of the production function parameters ↵

kit , lit , mit to calculate estimated output elasticities for each input ebl,i , ebk,i , ebm,i , as in (1), and then di . We report our results use the optimality condition (2) to recover estimated ‘inverse markups’ 1/µ di in Table 3 in the main text. for ebl,i , ebk,i , ebm,i in Table 2 in the main text and our results for 1/µ

A.4

Nonparametric productivity distribution

We now show how to use our model to recover the exact nonparametric distribution of producer-level productivity ai (s) given data on producer market shares !i (s). This procedure uses the structure of the model, but makes no parametric assumptions about the distribution of productivity. The main idea is fairly intuitive: we simply back out for each producer and sector the productivity draws that are needed to rationalize that producer’s and sector’s relative size. To do this, begin by recalling that for producer i in sector s the inverse markup is given by ✓ ◆ 1 1 1 1 = !iH (s) , ✓ µH (s) i

(3)

and that we can write the market share !iH (s) as !iH (s) = P

or !iH (s) or

pH (s)1 = Pi H 1 i pi (s)

H 1 i pi (s)

⇥P

1 pH i (s) P F 1 + ⌧1 i pi (s) H 1 i pi (s)

!iH (s) = ! ˜ iH (s) ⇥ (1

P

H 1 i pi (s) P + ⌧1

,

F 1 i pi (s)

,

! F (s)) ,

(4)

where ! ˜ iH (s) is producer i’s share of sales among only domestic firms in sector s and 1

! F (s) is

the share of spending on domestic firms in that sector. Both of these terms come directly from the data. The first term can be written ! ˜ iH (s) = P

µH i (s)/ai (s) i

1

µH i (s)/ai (s)

1

(5)

H where we simply use the definition of the markup to write pH i (s) = µi (s)W/ai (s). Thus, given

parameter values

and ✓, we can use an iterative procedure to recover the ai (s) of domestic

producers that exactly rationalizes the observed market share data !iH (s) and 1

! F (s). The

iterations are as follows: 1. Given data on !iH (s) and ! F (s) we construct ! ˜ iH (s) from (4) and calculate µH i (s) from (3). Now set ai (s) = z(s)xi (s) and impose the normalization mini [xi (s)] = 1 for each s (we can always multiply the numerator and denominator on the right-hand-side of (5) by a sectorspecific constant). 5

2. Guess productivities a0i (s). Then update the guess a0i (s) ! a1i (s) by iterating on the mapping ! 1 1 H (s)1 1 µ i ak+1 (s) = , k = 0, 1, . . . P i k (s) 1 ! ˜ iH (s) i µH (s)/a i i and iterate on this until convergence.

To further compute z(s), we repeat this argument at the sectoral level. Specifically, we use ✓ H ◆1 pi (s) H !i (s) = , p(s) thus p(s) =

X ✓ µH (s) ◆1 i xi (s)z(s) i

where we have already recovered

!

1 1

=:

⌅H (s) , z(s)

⌅H (s)

in the previous within-sector iteration, that is ! 1 1 X ✓ µH (s) ◆1 H i ⌅ (s) = . xi (s) i

Finally, note that the sectoral share !(s) = (p(s)/P )1

✓

, thus we can again use an iterative proce-

dure to find z(s) using observed data of sectoral expenditure shares (⌅H (s)/z(s))1

✓

H 1 0 (⌅ (s)/z(s))

✓

!(s) = R 1

ds

.

Since we are primarily interested in the tail properties of the recovered nonparametric productivity distribution, we calculate standard measures of the tail exponent of the recovered distribution and compare this summary statistic to its counterpart in our benchmark model, i.e., our original Pareto shape parameter. Specifically, to estimate the tail exponent implied by the recovered distribution we follow Gabaix and Ibragimov (2011) and run a log-rank regression. The basic idea is that for any power law distributed randomly distributed data, we have log(r

r¯) = constant

⇠x log X(r) + noise

where r is the ranking of observation X(r) . The slope coefficient ⇠bx then corresponds to the Pareto

shape parameter. Gabaix and Ibragimov suggest using the correction r¯ = 1/2 to reduce smallsample bias, but our results are almost identical when we use r¯ = 0. Our estimate implies a shape parameter ⇠ˆx = 3.46 with a standard error 0.02. We apply the same regression to sectoral productivity1 Z(r) , and find an estimate ⇠bz = 0.27

with a standard error 0.01. Both cases indicate that, if anything, the nonparametric productivity

distribution is fatter tailed than our benchmark Pareto distribution (which has ⇠x = 4.53 and ⇠z = 0.56). Our benchmark results are thus conservative in the sense that, if anything, we somewhat understate the amount of misallocation in the data.

1 We leave out the bottom 25% of sectoral observations, these look more lognormal and our interest here is in the right tail of the distribution. We find ⇠bz = 0.14 if we include all sectoral observations.

6

B

Robustness experiments and sensitivity analysis

Here we provide further details of the robustness experiments reported in the main text along with related sensitivity analysis. Unless stated otherwise, for each experiment we recalibrate the trade cost ⌧ , export fixed cost fx , and correlation parameter ⌧ (⇢) so that the Home country continues to have an aggregate import share of 0.38, fraction of exporters 0.25 and trade elasticity 4, as in our benchmark model. The full set of parameters used for each experiment are reported in Table A3. The target moments and the moments implied by each model are reported in Table A4. The gains from trade and statistics on markup dispersion for each model are reported in Table A5.

B.1

Correlated xi (s), x⇤i (s)

For this experiment (which we refer to in the main text as the alternative model), we allow for cross-country correlation in both sectoral productivity draws and in idiosyncratic producer-level productivity draws. Specifically, we assume the cross-country joint distribution of sectoral productivity HZ (z, z ⇤ ) = CZ (FZ (z), FZ (z ⇤ )) and the cross-country joint distribution of idiosyncratic productivity HX (x, x⇤ ) = CX (FX (x), FX (x⇤ )) are both linked via a Gumbel copula but with distinct

correlation coefficients, ⌧z (⇢) and ⌧x (⇢). As in the benchmark model, we choose the sectoral correlation ⌧z (⇢) so that the model implies a trade elasticity of 4. We choose the cross-country correlation in idiosyncratic draws ⌧x (⇢) so that the model reproduces the cross-sectional relationship between domestic producer concentration and import penetration that we see in the data, i.e., that sectors with high import penetration are also sectors with relatively high concentration amongst domestic producers. As shown in the last row of Table A4, our benchmark model implies a mild association, the slope coefficient in a regression of sector import penetration on sector domestic HH indexes is 0.08, low relative to the 0.21 in the data. To match this regression coefficient, the alternative model needs a modest amount of cross-country correlation in in idiosyncratic draws, ⌧x (⇢) = 0.22 (with a correspondingly slightly lower correlation in sectoral draws, ⌧z (⇢) = 0.90). This version of the model otherwise fits the data about as well as the benchmark model. As shown in Table A5, it implies slightly larger pro-competitive gains from trade.

B.2

Labor wedges

For this experiment we assume there is a distribution of producer-level labor market distortions that act like labor input taxes, putting a wedge between labor’s marginal product and its factor cost. Specifically, we assume a producer with productivity a faces an input tax t(a) :=

a⌧l , 1 + a⌧l

and pays (1 + t(a))W for each unit of labor hired. The price a Home producer with productivity ai (s) sets in its domestic market is then pH i (s)

⇣ "H (s) ⌘ 1 + t(a (s)) i = Hi W, a (s) "i (s) 1 i 7

(6)

where "H i (s) > 1 is the demand elasticity facing the firm in its domestic market, which satisfies the same formula as in the main text. We calibrate the sensitivity parameter ⌧l so that our model matches the ratio of the average producer labor share to the aggregate labor share that we observe in the data. In the data, the average producer labor share is 1.35 times the aggregate labor share. This requires ⌧l = 0.003, implying that the labor taxes and productivity are positively related, albeit weakly so. As shown in Table A5, the gains from trade and the pro-competitive gains from trade are quite similar to the benchmark model.

B.3

Heterogeneous tari↵s Figure 1: Distribution of tari↵ rates t(s) across 7-digit Taiwanese manufacturing sectors

100

90

80

70

60

50

40

30

20

10

0 0

0.05

0.1

0.15

0.2

0.25

For this experiment we assume that in each sector s there is a distortionary tari↵ t(s), common to every firm in that sector. For simplicity we assume that the tari↵ revenues are rebated lump-sum to the representative consumer. The price a Home producer with productivity ai (s) sets in its domestic market is then pH i (s) =

⇣ "H (s) ⌘ W 1 i , 1 t(s) "H (s) 1 ai (s) i

(7)

We assume that the tari↵s t(s) 2 [0, 1] are drawn IID Beta(a, b) across sectors. We estimate the

parameters of this Beta distribution by maximum likelihood using detailed tari↵ data for Taiwanese 7-digit manufacturing sectors. The maximum likelihood point estimates are a = 2.3 and b = 35,

implying a quite skewed distribution with mean tari↵ of a/(a + b) = 0.062 and a standard deviation p of ab/((a + b)2 (a + b + 1)) = 0.039. Figure 1 plots the empirical histogram of tari↵s in the Taiwanese data against the density function of a Beta distribution with these parameters. As 8

reported in Table A5, both the total gains from trade and the pro-competitive gains are somewhat larger than in the benchmark.

B.4

Bertrand competition

For this experiment we re-solve the model under the assumption that producers compete by simultaneously choosing prices (Bertrand) rather than simultaneously choosing quantities (Cournot). This changes the model set-up in only one way. The demand elasticity facing producer i in sector s is no longer a harmonic weighted average, of ✓ and "i (s) = !i (s)✓ + (1

but is instead a simple arithmetic weighted average,

!i (s)) . With this specification the results are similar to the benchmark.

The Bertrand model implies somewhat lower markup dispersion than the Cournot model but also implies a larger change in markup dispersion when opening to trade — and hence a larger reduction in misallocation. One problem with the Bertrand model is that it implies a negative correlation between domestic sectoral concentration and domestic import penetration, i.e., in the Bertrand model highly concentrated sectors tend to have low import penetration, the opposite of what we see in the data.

B.5

Sensitivity to

In our benchmark model, we set the within-sector elasticity of substitution to robustness exercise we consider

= 5 and

= 10. For this

= 20. For each case we reset the across-sector elasticity

✓ so that a regression of inverse markups on market share continues to have a slope of the benchmark, i.e., for each a higher ✓ = 1.37 when With

we set ✓ = (1/ + 0.68)

1,

giving a lower ✓ = 1.13 when

0.68, as in = 5 and

= 20.

= 5 we find that the model cannot produce a trade elasticity of 4, even setting ⌧ (⇢) =

0.999 (e↵ectively perfect correlation) gives a low trade elasticity of 2.38. In addition, as shown in the last few rows of Table A4, the

= 5 version of the model has a number of problems replicating

the facts on intra-industry trade and import share dispersion, it implies: (i) too much intra-industry trade, (ii) too little dispersion in import shares, (iii) too strong an association between sector import shares and size, and (iv) a negative correlation between sector concentration and import penetration. With

= 20, we can match a trade elasticity of 4 with correlation ⌧ (⇢) = 0.85, somewhat lower

than our benchmark. As shown in Table A4, the

= 20 version of the model also has problems

replicating the facts on intra-industry trade and import share dispersion, but, roughly speaking, it gets them the opposite way round to the

= 5 version. Specifically, the

= 20 version implies

(i) too little intra-industry trade, (ii) slightly too much dispersion in import shares, (iii) too weak an association between sector import shares and size, and (iv) too strong an association between sector concentration and import penetration. In short, very low values like

= 5 and very high values like

= 20 are at odds with the data.

To narrow in on an estimate of , we conducted a grid search and varied the value of

over the

range 5 to 20 and re-estimated the entire model for each such . When we did so, we found that the value of the objective function (squared deviation between the moments in the model and the 9

data) is minimized at a value of

between about 8 and 12. The objective is, however, extremely

flat in this region so there is not much information in our data that would allow us to narrow in any further. Given this, we decided to simply set

= 10, a standard number in the macro literature. Our

results on the gains from trade are in any case very robust to perturbations of

B.6

in the range 8-12.

No fixed costs

For this experiment, we solve our model assuming that fixed costs are zero, fd = fx = 0. In this version of the model, all producers operate in both markets. Thus the number of domestic producers in each country in sector s is just given by the Geometric draw n(s) for that sector. As shown in Table A5, this version of the model yields almost identical gains from trade as the benchmark. Shutting down these extensive margins makes little di↵erence because the typical firm near the margin of operating or not is very small and has negligible impact on the aggregate outcomes.

B.7

Gaussian copula

For this experiment we resolve the model using a Gaussian copula to model the cross-country correlation in sectoral productivity draws, specifically C(u, u⇤ ) = where

2,⇢ (

1

(u),

1

(u⇤ ))

(x) denotes the CDF of the standard Normal distribution and

(8) 2,⇢ (x, x

⇤)

denotes the

standard bivariate Normal distribution with linear correlation coefficient ⇢ 2 ( 1, 1). To compare results to the benchmark Gumbel copula, we map the linear correlation coefficient into our preferred Kendall correlation coefficient, which for the Gaussian copula is ⌧ (⇢) = 2 arcsin(⇢)/⇡. To match a trade elasticity of 4 requires ⌧ (⇢) = 0.97, up slightly from the benchmark. As shown in Table A5, this version of the model yields very similar results to our benchmark model. In this sense, the functional form of the copula per se does not seem to matter much for our results, instead, as discussed at length in the main text, it is the amount of correlation ⌧ (⇢) in cross-country productivity draws that matters.

B.8

Uncorrelated n(s), n⇤ (s)

For this experiment, for each sector s we independently draw n(s) producers for the Home country and n⇤ (s) producers for the Foreign country (each drawn from the same Geometric marginal distribution as in the benchmark model). With independent draws for n(s), n⇤ (s) we find that the model cannot produce a trade elasticity of 4, even setting ⌧ (⇢) = 0.999 (e↵ectively perfect correlation) gives a trade elasticity of 2.47. As a consequence of this low trade elasticity, this version of the model implies very large total gains from trade. As shown in the last few rows of Table A4, with independent draws for n(s), n⇤ (s) the model also implies that there there is no relationship between a sector’s import share and its size, an 10

implication which is clearly at odds with the data, and moreover the model also implies too strong an association between sector concentration and import penetration.

B.9

5-digit sectors

For this last robustness experiment, we recalibrate our model to 5-digit rather than 7-digit data. The second-last column of Table A4 reports the 5-digit counterparts of our usual 7-digit moments in the Taiwanese data while the last column of Table A4 reports the model moments when calibrated to this 5-digit data. At this higher level of aggregation there is less concentration in sectoral shares than there is at the 7-digit level and hence there is less measured misallocation. The productivity losses due to markups are 5.8%, down from the 6.7% for our benchmark model calibrated to 7-digit data. The total gains from trade remain about 12%, as in the benchmark, but since there is less measured misallocation, the pro-competitive e↵ects are weaker, contributing 0.3% down from our benchmark. Thus, consistent with our earlier results, we see that the pro-competitive gains from trade are smaller when product market distortions are small. To maintain comparability with our other results, for this experiment we have kept the acrosssector elasticity fixed at ✓ = 1.28, which is arguably quite high for 5-digit data.2 With a lower value for ✓ (e.g., with Cobb-Douglas ✓ = 1) measured misallocation is higher and the pro-competitive gains from trade are correspondingly higher also.

B.10

Fixed N competitors per sector

To further highlight the role of cross-country correlation in sectoral productivity draws, we have solved a simplified version of our model with the following structure: a fixed number of producers N per sector (the same in both countries), no fixed costs of operating (so all N producers operate), and either perfectly dependent cross-country draws, ⌧ (⇢) = 1, or perfectly independent cross-country draws, ⌧ (⇢) = 0. We compare autarky to free trade — i.e., no net trade costs, ⌧ = 1, and no fixed costs of exporting, fx = 0. Panel A of Table A6 shows results for the case of no idiosyncratic productivity draws, xi (s) = 1 for all i, s. Consider the case N = 1, so that under autarky there is a single monopolist in each sector. In this case there is no misallocation in autarky (there is neither within-sector nor acrosssector markup dispersion). Now with free trade there are two producers in each sector (in this sense, it is as if the country size doubles). The e↵ects on misallocation crucially depend on the cross-country correlation in sectoral productivity. If sectoral productivity draws are independent across countries, then typically one producer gains market share at the expense of the other, and, crucially, this pattern varies across sectors depending on the particular pairs of productivity draws. This creates markup dispersion and hence with free trade there is misallocation whereas there was no misallocation in autarky. Thus the gains from trade will be less than if markups were constant (i.e., aggregate productivity increases by less than first-best productivity). By contrast, if sectoral 2

A 5-digit sector in Taiwan best corresponds to a 4-digit sector in the US.

11

productivity draws are perfectly correlated across countries, then the two producers (who have equal productivity) split the market between them and this happens exactly the same way in each sector, hence in this case trade does not lead to misallocation. Put di↵erently, having ⌧ (⇢) = 1 perfectly mitigates the increase in misallocation that would otherwise happen. Notice that the extent of markup dispersion and hence misallocation created when sectoral productivity draws are independent is decreasing in N — and steeply decreasing in N at that. With independent draws and N = 1, free trade creates productivity losses of 13.2% relative to the first-best, with N = 2 the losses are much smaller, 0.8% and with N = 10 the losses are down to 0.02%. Panel B of Table A6 shows the same exercise but now with idiosyncratic productivity draws, as in the benchmark model. We again see that for low N opening to trade creates misallocation and that this misallocation is mitigated by correlated sectoral productivity draws. By the time we get to N = 10 (which is similar to our benchmark model, which has a median of about 10 producers per sector per country), opening to free trade reduces misallocation if sectoral draws are correlated.

C

Extensions

Our benchmark model makes several stark simplifying assumptions: (i) labor is the only factor of production and is in inelastic supply, (ii) the two countries Home and Foreign are symmetric at the aggregate level, and (iii) there is an exogenous number of competitors per sector. Here we provide further details on extensions of our benchmark model that relax these assumptions. Since the main text already discusses the results from these extensions at some length, here we focus on recording additional details that were omitted from the main text to save space.

C.1

Capital accumulation and elastic labor supply

In the benchmark model, the only source of pro-competitive gains from trade is changes in markup dispersion. Changes in the level of the aggregate markup µ have no welfare implications. But with capital accumulation and/or elastic labor supply, the aggregate markup µ acts like a distortionary wedge a↵ecting investment and labor supply decisions, and, because of this, a reduction in the aggregate markup increases welfare beyond the increases associated with a reduction in markup dispersion. Setup.

To illustrate this, we solve a simple dynamic extension of our benchmark model. SpecifP1 t ically, we suppose the representative consumer has intertemporal preferences U (Ct , Lt ) t=0 over aggregate consumption Ct and labor Lt , that capital is accumulated according to Kt+1 = (1

)Kt + It , and that individual producers have production function y = ak ↵ l1

↵.

We then

have a standard two-country representative consumer economy with aggregate production function ˜ 1 ↵ where At is aggregate productivity (TFP) as in the main text and where L ˜ t is Yt = At Kt↵ L t aggregate employment net of fixed costs.

12

Aggregate markup distortions.

Using the representative consumer’s optimality conditions for

capital accumulation and labor supply and the firms’ optimal input demands gives the equilibrium conditions Uc,t = Uc,t+1 and

⇣ 1 Yt+1 ↵ +1 µt+1 Kt+1

Ul,t Wt 1 = = (1 Uc,t Pt µt

↵)

⌘

,

(9)

Yt , ˜t L

(10)

where µt is the aggregate markup as in the main text. High aggregate markups thus act like distortionary capital and labor income taxes and reduce output relative to its efficient level. Parameterization.

To quantify the additional welfare e↵ects of changes in the aggregate markup,

we solve this version of the model assuming utility function U (C, L) = log C discount factor

= 0.96, depreciation rate

L1+⌘ 1+⌘

and assuming

= 0.1 and output elasticity of capital ↵ = 1/3. We

report results for various elasticities of labor supply ⌘. We start the economy in autarky and then compute the transition to a new steady-state corresponding to the Taiwan benchmark. We measure the welfare gains as the consumption compensating variation taking into account the dynamics of consumption and employment during the transition to the new steady-state. Results.

The first column of Table A7 shows what happens in a standard model with constant

markups if TFP increases by 10.2%, i.e., the benchmark increase in first-best TFP. Physical capital, output, and consumption all increase by 15.3%, i.e., by 1/(1

↵) = 1.5 times the increase in TFP.

Aggregate labor does not change because utility is log in consumption so that the income and substitution e↵ects implied by the change in TFP exactly cancel out. The measured welfare gain is slightly less than the long-run increase in aggregate consumption because we take the transitional dynamics into account. The next column shows the corresponding results in our model with variable markups but where we hold the aggregate markup unchanged. Thus TFP increases by 12% because in addition to the first-best 10.2% there are now pro-competitive gains of 1.8%. Aggregate labor is again constant because of log utility and because the aggregate markup is held fixed. Thus the additional gains here are entirely because capital accumulation magnifies the TFP gains. The remaining columns show results when we also allow the aggregate markup to change, falling by 2.8% from autarky to the new steady-state. We report results for various choices of the Frisch elasticity of labor supply 1/⌘. If labor supply is inelastic, so the fall in the aggregate markup a↵ects capital accumulation alone, welfare increases by 17.4%. This gain is larger than the 16.3% we had when only TFP changes and the additional gain of 1.1% is entirely due to the e↵ect of the change in the aggregate markup and hence this extra 1.1% is entirely due to pro-competitive e↵ects, making for a total pro-competitive gain of some 3%. Elastic labor supply magnifies these gains yet further. With a Frisch elasticity of 1, the pro-competitive gains rise to 3.3% (as shown in Table A7, the size of the pro-competitive gains are not sensitive to a Frisch elasticity in the range 0.5 to 2). In short,

13

we see that with elastic factor supply the relative importance of the pro-competitive e↵ects is larger than in our benchmark model.

C.2

Asymmetric countries

Our benchmark model assumes trade between two symmetric countries. We now relax this and consider trade between countries that di↵er in size and/or productivity. ¯ A¯⇤ denote Home and Foreign Let L, L⇤ denote Home and Foreign labor forces and let A, ¯ i (s)li (s) economy-wide productivity — that is, Home producers now have technology yi (s) = Aa Setup.

and Foreign producers have yi⇤ (s) = A¯⇤ a⇤i (s)li⇤ (s). We normalize L = 1 and A¯ = 1 and consider various L⇤ and A¯⇤ . In our benchmark model, aggregate symmetry implied that the wage in each country was the same so that by choosing the Home wage as numeraire we simply had W = W ⇤ = 1 along with symmetric price levels P = P ⇤ and symmetric productivities A = A⇤ so that the real wage in both countries was 1/P and the aggregate (economy-wide) markup in both countries was µ = P A. We continue to choose the Home country wage as numeraire, W = 1, but with asymmetric countries now have to solve for the Foreign wage W ⇤ in equilibrium. Intuitively, W ⇤ has to adjust to ensure that trade between the two countries is balanced. With asymmetric countries, the equilibrium price levels P, P ⇤ and aggregate productivities A, A⇤ likewise di↵er across countries. We then have Home real wage 1/P and aggregate markup µ = P A and Foreign real wage W ⇤ /P ⇤ and aggregate markup µ⇤ = P ⇤ A⇤ /W ⇤ . We consider L⇤ = 2 and L⇤ = 10 times as large as Home, holding economywide productivity the same in both countries, and then consider A¯⇤ = 2 and A¯⇤ = 10 times as great Parameterization.

as Home productivity, now holding the labor force the same in both countries. For each of these four experiments, we recalibrate the model so that, for the Home country, we reproduce the degree of openness of the Taiwan benchmark — in particular, we choose the proportional trade cost ⌧ , export fixed cost fx , and correlation parameter ⌧ (⇢) so that the Home country continues to have an aggregate import share of 0.38, fraction of exporters 0.25 and trade elasticity 4. Table A8 reports the full set of parameters used for each experiment, Table A9 reports the target moments and their model counterparts for both Home and Foreign countries for each experiment, and Table A10 reports the gains from trade and statistics on markup dispersion for both Home and Foreign countries for each experiment. In addition to our usual aggregate statistics, in Table A10 we also report the relative real wage expressed as the real wage of Foreign to Home, that is (W ⇤ /P ⇤ )/(W/P ) = (A⇤ /A)/(µ⇤ /µ).

C.3

Free entry

We now discuss in somewhat greater detail a version of our model with free entry and an endogenous number of competitors per sector. We assume that entry is not directed at a particular sector: after paying a sunk entry cost, a firm learns the productivity with which it operates, as in Melitz (2003),

14

as well as the sector to which it is assigned. We also assume that there are no fixed costs of operating or exporting in any given period. Instead, we assume that a firm’s productivity is drawn from a discrete distribution which includes a mass point at zero. Setup.

The productivity of a firm in sector s 2 [0, 1] is given by a world component, common to

both countries, z(s), and a firm-specific component. In addition, we assume a gap, u(s), between

the productivity with which a firm produces for its domestic market and that with which it produces in its export market. Greater dispersion in u(s) reduces the amount of head-to-head competition between Home and Foreign producers, lowers the aggregate trade elasticity, and thus has the same role as reducing the correlation between sectoral productivity draws in our benchmark model. Specifically, let u(s) denote the productivity gap of Home producers in sector s and u⇤ (s) denote the productivity gap of Foreign producers in sector s. There is an unlimited number of potential entrants. To enter, a firm pays a sunk cost fe that allows it to draw (i) a sector s in which to operate and (ii) idiosyncratic productivity xi (s) 2 {0, 1, x ¯}. A Home firm in sector s with idiosyncratic productivity xi (s) produces for its domestic market with overall productivity aH i (s) = z(s)u(s)xi (s)

and produces for its export market with overall productivity a⇤H i (s) = z(s)xi (s)/⌧ where ⌧ is the gross trade cost. Similarly, a Foreign firm in sector s with idiosyncratic productivity x⇤i (s) produces ⇤ ⇤ for its domestic market with overall labor productivity a⇤F i (s) = z(s)u (s)xi (s) and produces for ⇤ its export market with overall productivity aF i (s) = z(s)xi (s)/⌧ .

Sector types.

Sectors di↵er in the probability that any individual entrant assigned to that sector

draws a particular productivity xi (s). To simplify computations, we assume a finite number of sector types k = 1, ..., K. A sector type is a pair ⌦1 (k), ⌦2 (k) where ⌦1 denotes the probability that an entrant is successful, i.e., that it draws xi (s) > 0, and ⌦2 denotes the probability that a successful entrant draws high productivity xi (s) = x ¯. We write ⌫(k) for the measure of sectors of P type k with k ⌫(k) = 1.

The special case of a single sector type, K = 1, is of particular significance since it implies that

there is no cross-country correlation in productivities — in this case the probability that a successful entrant gets a high productivity draw x ¯ is the same in all sectors and such draws are IID across producers. In the more general case with heterogeneous sector types, K > 1, there is cross-country correlation in productivities since the sector type k is the same for both countries so that producers in a sector with high ⌦2 (k) have a common high probability of drawing x ¯, irrespective of which country they are located in. We think of these sectoral di↵erences as being primarily technological in nature and thus invariant across countries. Timing.

The timing of entry is as follows:

1. An entrant draws a sector and thus implicitly a type k 2 {1, ..., K}. The type k determines both the probability of any individual entrant drawing a particular productivity realization as well as the distribution of other competitors it will face.

15

2. The entrant draws a random variable that determines whether it is successful (with probability ⌦1 (k)) and can thus begin operating, or whether it exits (with probability 1

⌦1 (k)).

3. Successful entrants then draw their productivity type. With probability ⌦2 (k) a successful entrant becomes a high-productivity producer (with xi (s) = x ¯), while with probability 1 ⌦2 (k) they become a low-productivity producer (with xi (s) = 1). Now let N be the measure of producers that actually enter. Recall that we assume entrants are uniformly distributed across sectors. Then since the total measure of sectors is 1, the number of successful entrants who produce in a sector of type k is a Binomial random variable with a success probability ⌦1 (k) and N trials. For each sector s 2 [0, 1], let k(s) denote that sector’s type and let n(s) denote the resulting number of producers. Likewise let n1 (s) and n2 (s) denote the number

of low-productivity producers (xi (s) = 1) and high-productivity producers (xi (s) = x ¯). Given the realization of n(s), n2 (s) is a Binomial random variable with a success probability of ⌦2 (k(s)) and n(s) trials. Finally, let n⇤1 (s) and n⇤2 (s) denote the number of Foreign producers of each type that produce in sector s To summarize, any individual sector s is characterized by (i) the number of competitors of each productivity type, n1 (s), n2 (s), n⇤1 (s), n⇤2 (s), (ii) the common productivity component of all producers (both Home and Foreign) operating in that sector, z(s), (iii) the productivity advantage of Home producers relative to importers in the Home market, u(s), and (iv) the productivity advantage of Foreign producers relative to Home exporters in the Foreign market, u⇤ (s). Production and pricing.

The rest of the model is essentially identical to the benchmark model

in the main text. For example, the final good is a CES aggregate of sector inputs Y =

✓Z

1

y(s)

✓ 1 ✓

ds

0

◆✓✓1

,

while sector output is a CES aggregate of the production of the various types of producers in each sector, which we now write

where

⇣ y(s) = n1 (s)y1H (s)

1

+ n2 (s)y2H (s)

1

+ n⇤1 (s)y1F (s)

1

+ n⇤2 (s)y2F (s)

1

⌘

1

,

is, as earlier, the elasticity of substitution within a sector and ✓ is the elasticity of sub-

stitution across sectors. Since there are no fixed costs of exporting or selling domestically, all n(s) producers operate in sector s. As in the benchmark model, the markup a firm charges is a function of the number of competitors of each productivity it competes with. For example, in its domestic market a Home firm with idiosyncratic productivity xi (s) in sector s has markup µH i (s) =

"H i (s) , H "i (s) 1

16

where "H i (s)

=

✓

1 !iH (s) ✓

!iH (s)

+ 1

1

◆

1

,

and where !iH (s) denotes their market share in their domestic market 0 11 H !iH (s)

=

µi (s) z(s)x i (s)u(s) @

p(s)

A

.

Similarly, a Home firm with idiosyncratic productivity xi (s) in sector s has export market share 0 ⇤H 11 µi (s)

!i⇤H (s)

z(s)x (s) =@ ⇤ i A p (s)

.

Given the markups, the Home price of the sector s composite satisfies p(s)1

1 = n1 (s)µH 1 (s)

+ ⌧1

1

(z(s)u(s))

1 n⇤1 (s)µF 1 (s)

1

z(s)

1 + n2 (s)µH 2 (s)

+ ⌧1

1

(z(s)u(s)¯ x)

1 n⇤2 (s)µF 2 (s)

1

(z(s)¯ x)

,

and the Foreign price of the sector s composite satisfies p⇤ (s)1

= ⌧1

1 n1 (s)µ⇤H 1 (s)

1 + n⇤1 (s)µ⇤F 1 (s)

z(s)

1

+ ⌧1

1 n2 (s)µ⇤H 2 (s)

1

1 + n⇤2 (s)µ⇤F 2 (s)

(z(s)u⇤ (s))

Expected profits of a potential entrant.

(z(s)¯ x)

1 1

(z(s)u⇤ (s)¯ x)

.

We now compute the expected profits of a firm that

contemplates entry. Such a firm has an equal probability of entering any one of the sectors. Recall that sectors di↵er in = (⌦1 , ⌦2 , u, u⇤ , z, n1 , n2 , n⇤1 , n⇤2 ) , where z, u, u⇤ are all independent random variables. Let F ( ) denote the distribution of producers over

and let ⇡1 ( ), ⇡2 ( ) denote respectively the profits of an entrant with idiosyncratic produc-

tivity x = 1 and x = x ¯ in sector . Since a potential entrant is equally likely to enter any sector, its expected profits are Z h ⇡e = ⌦1 ( ) (1

where

1(

) is equal to

⌦2 ( ))⇡1 (

1(

)) + ⌦2 ( )⇡2 (

2(

except that n1 is replaced by n1 + 1 and

)) 2(

i W fe ( ) dF ( ) , ) is equal to

except that n2

is replaced by n2 + 1 — i.e., a potential entrant recognizes that its entry, if successful, will change the number of producers and thus alter the industry equilibrium by changing the price p(s) and p⇤ (s) of that sector’s composite in both countries. This expression says that the expected profits conditional on entering a sector

are given by

⌦1 ( ), the probability of successful entry into that sector, times the expected profits conditional on entry, which in turn depend on the probability of getting the higher productivity draw, ⌦2 ( ). This expression also reveals why we simplify the productivity distribution for this free-entry version of the model: the distribution F ( ) is a high-dimensional object which we can only integrate accurately when we use the simpler productivity distribution assumed here. 17

Free entry condition.

Expected profits ⇡e are implicitly a function of N , the measure of entrants

— since N characterizes the Binomial distribution of the number of producers of each type in a given sector — as well as the trade cost ⌧ , which determines how much the producer is making from its export sales as well as how much competition it faces from Foreign producers. We pin down the measure of entrants N in equilibrium by setting ⇡e (N, ⌧ ) = 0 , for any given level of the trade cost ⌧ . Notice here that we implicitly allow the fixed cost of entering, to vary with the sector to which a producer is assigned. More specifically, we assume that the fixed cost is proportional to the sector’s productivity, fe (s) = fe ⇥ z(s)✓ degree 1 in z(s)✓

1

1

for some constant fe > 0. Sectoral profits are homogeneous of

so this assumption simply implies that the fixed cost scales up with the profits

of the sector to which the entrant is assigned. Model with collusion: setup.

We also report results based on a model in which all high-

productivity producers from a given country and sector are able to collude and maximize joint profits. We assume that producers in a fraction of sectors collude, while the rest face the same problem as that described above. Consider the problem of the colluding Home producers selling in Home. They choose their price to maximize joint profits

h H n2 (s) pH 2 (s)y2 (s)

taking as given the inverse demand curve pH 2 (s)

=

✓

y2H (s) y(s)

◆

1

i W l2H (s) , ✓

y(s) Y

◆

1 ✓

P,

and recognizing that ⇣ y(s) = n1 (s)y1H (s)

1

+ n2 (s)y2H (s)

1

+ n⇤1 (s)y1F (s)

1

+ n⇤2 (s)y2F (s)

The optimal markup is now given by 1 = H µ2 (s)

1

✓

1 ✓

1

◆

1

⌘

1

.

n2 (s)!2H (s) ,

and now reflects the overall sectoral share n2 (s)!2H (s) of the colluding producers, not each individual producer’s share in isolation. Parameterization.

We continue to set

= 10 and ✓ = 1.28 as in our benchmark model to allow

comparability of results. We again choose the trade cost, ⌧ , to match Taiwan’s import share of 0.38. We assume the productivity gaps u(s), u⇤ (s) are IID logormal with variance the dispersion to match a trade elasticity of 4. 18

2 u

and choose

We consider two variants of the model, (i) with a single sector type K = 1 (and hence two probabilities ⌦1 , ⌦2 ) and (ii) with heterogeneous sector types, that, as we will see, does a better job of matching the dispersion in concentration we see in the data. For the latter we have found that allowing for 3 values for ⌦1 (k) and another 3 values for ⌦2 (k) works reasonably. In this case, we have to determine these 6 values plus 8 = 32

1 values for the measures ⌫(k) of each sector type.

For both variants, we choose the entry cost fe , the productivity advantage of type 2 producers x ¯, and the distribution of ⌦1 (k), ⌦2 (k) across sectors targeting the same set of concentration moments we targeted for the benchmark model. Finally, we choose the dispersion of sectoral productivity z(s) to match the amount of concentration in output and employment across sectors. Specifically, we assume a Pareto distribution of z(s) and choose the shape parameter to match the fraction of value added (employment) accounted for by the top 1% and 5% of sectors. Table A11 reports the full set of parameters used for each free-entry experiment, Table A12 reports the target moments and their model counterparts, and Table A13 reports the gains from trade and statistics on markup dispersion for each free-entry experiment. Single sector type: results.

With a single sector type, K = 1, the free-entry model implies

total gains of 6.4% of which 1.5% is due to pro-competitive e↵ects. The free-entry model implies less misallocation in autarky than in the benchmark model but also implies a greater reduction in misallocation when the economy opens to trade. Misallocation relative to autarky falls by just under a half. As shown in Table A12, this version of the model is not able to reproduce the amount of dispersion in concentration that we see in the data. Consequently, as shown in Table A13, this version of the model also implies very little dispersion in sectoral markups as compared to the data. Heterogeneous sector types: results.

By allowing sectoral di↵erences in the probability of a

successful entrant drawing the high-productivity x ¯, the model produces more dispersion in concentration and hence more dispersion in sectoral markups. We have found that, as shown in Table A12, with nine sector types the model does a considerably better job at matching the facts on dispersion in sectoral concentration. This version of the model implies total gains of 7.2% of which 1.2% is due to pro-competitive e↵ects. Nonetheless, this version of the model still produces too little dispersion in sectoral markups. For example, the ratio of the 95th percentile of markups to that of the median is equal to only 1.16 in the model, much lower than the 1.56 in the data. To better match the dispersion in sectoral markups, we turn to the extension with collusion outlined above. With collusion the model produces considerably more dispersion in sectoral markups. For example, when 25% of sectors collude, the ratio of the 95th to the median markup increases from 1.16 to 1.30, still smaller than in the data but now almost double the dispersion of the nine sector model without collusion. With 25% collusion, the model implies total gains of 11.2% of which 3.9% is due to pro-competitive e↵ects. Thus this version of the model implies total gains about the same as the benchmark but gives a much larger share to the pro-competitive e↵ect. In short, we again see that the pro-competitive gains from trade are large when product market distortions are large to begin with. 19

References Ackerberg, Daniel A., Kevin Caves, and Garth Frazer, “Structural Identification of Production Functions,” 2006. UCLA working paper. Bernard, Andrew B., Stephen J. Redding, and Peter Schott, “Multiple-Product Firms and Product Switching,” American Economic Review, March 2010, 100 (1), 70–97. De Loecker, Jan and Frederic Warzynski, “Markups and Firm-Level Export Status,” American Economic Review, October 2012, 102 (6), 2437–2471. Gabaix, Xavier and Rustam Ibragimov, “Rank

1/2: A Simple Way to Improve the OLS

Estimation of Tail Exponents,” Journal of Business and Economic Statistics, 2011, 29 (1), 24– 39. Levinsohn, James and Amil Petrin, “Estimating Production Functions using Inputs to Control for Unobservables,” Review of Economic Studies, April 2003, 70 (2), 317–342. Melitz, Marc J., “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity,” Econometrica, November 2003, 71 (6), 1695–1725. Olley, G. Steven and Ariel Pakes, “The Dynamics of Productivity in the Telecommunications Equipment Industry.,” Econometrica, November 1996, 64 (6), 1263–1297.

20

7"digit

5"digit

3141000*"*mini"computer 3141010*"*work"station 3141021*"*desktop*computer 3141022*"*laptop*computer 3141023*"*notebook*computer 3141024*"*palmtop*computer 3141025*"*pen"based*computer 3141026*"*hand*held*computer 3141027*"*electronic*dictionary

31410*"*computers

3"digit 314:*computers*and*storage*equipment

Panel&A:&An&Example&of&Product&Classification

textile apparel leather lumber furniture paper printing chemical*materials chemical*products petroleum rubber plastics clay/glass/stone primary*metal fabricated*metal industrial*machinery computer/electronics electronic*parts electrical*machinery transportation instruments

2"digit

16 10 4 6 4 6 3 7 9 2 3 7 18 14 14 29 11 6 11 12 7

4"digit

76 39 33 15 12 23 4 152 83 12 16 34 47 99 65 163 136 72 125 99 70

7"digit (sector)

Panel&B:&Distribution&of&Sectors&and&Industries

Table&A1:&Data&Description&and&Product&Classification

1.17 3.73 13.82 0.16 0.41 0.92 2 10 52

p10+inverse+HH p50+inverse+HH p90+inverse+HH

p10+top+share p50+top+share p90+top+share

p10+number+producers p50+number+producers p90+number+producers

Across§or,concentration

3 11 56

0.16 0.40 0.88

1.28 3.85 14.36

0.41 0.65 0.24 0.47

fraction+sales+by+top+0.01+firms fraction+sales+by+top+0.05+firms fraction+wages+(same)+top+0.01+producers fraction+wages+(same)+top+0.05+producers

0.04 0.004 0.02 0.19 0.58 0.11

mean+share median+share p75+share p95+share p99+share std+dev+share

0.04 0.005 0.02 0.19 0.59 0.11

Size,distribution,of,producers,based,on,domestic,sales

0.26 0.52 0.11 0.32

Distribution,of,sectoral,shares,,domestic,sales

7.02 3.85 0.45 0.40

fraction+sales+by+top+0.01+sectors fraction+sales+by+top+0.05+sectors fraction+wages+(same)+top+0.01+sectors fraction+wages+(same)+top+0.05+sectors

7.25 3.92 0.45 0.40

mean+inverse+HH median+inverse+HH mean+top+share median+top+share

Plant Size,distribution,of,sectors,based,on,domestic,sales

Firm

Within§or,concentration,,domestic,sales

Plant

Table&A2:&Plant-Level&and&Firm-Level&Concentration

0.41 0.65 0.32 0.56

0.27 0.53 0.15 0.36

Firm

within§or,elasticity,of,substitution across§or,elasticity,of,substitution pareto,shape,parameter,,idiosyncratic,productivity pareto,shape,parameter,,sector,productivity geometric,parameter,,number,producers,per,sector fixed,cost,of,domestic,operations fixed,cost,of,export,operations trade,cost kendall's,tau,for,sectoral,draws,

kendall's,tau,for,idiosyncratic,draws sensitivity,of,labor,wedge,to,productivity mean,tariff std,dev,tariffs

Additional%parameters%/%moments

γ θ ξ_x ξ_z ζ f_d f_x τ τ(ρ)

Main%parameters

0 0 0 0

10 1.28 4.53 0.56 0.043 0.0043 0.211 1.128 0.93

Benchmark

0.22 0 0 0

10 1.28 4.53 0.56 0.043 0.0043 0.211 1.129 0.90

Alternative

0 0.003 0 0

10 1.28 4.53 0.56 0.043 0.0043 0.243 1.128 0.91

Labor2wedges

0 0 0.062 0.039

10 1.28 4.53 0.56 0.043 0.0043 0.199 1.067 0.92

Tariffs

0 0 0 0

10 1.28 4.53 0.56 0.043 0.0043 0.109 1.132 0.90

Bertrand

0 0 0 0

5 1.13 2.70 0.25 0.044 0.0185 0.710 1.214 1.00

Low2γ

Table2A3:2Parameters2for2Robustness2Experiments

0 0 0 0

20 1.37 5.65 0.74 0.032 0.0035 0.018 1.137 0.85

High2γ

0 0 0 0

10 1.28 4.53 0.56 0.043 0 0 1.137 0.93

0 0 0 0

10 1.28 4.53 0.56 0.043 0.0043 0.195 1.129 0.97

0 0 0 0

10 1.28 4.53 0.56 0.043 0.0043 0.050 1.223 1.00

No2fix2costs Gauss.2copula n(s),n*(s)

0 0 0 0

10 1.28 5.60 0.56 0.020 1e&7 0.049 1.130 0.91

5Edigit

2 10 52

p10%number%producers p50%number%producers p90%number%producers

0.81 0.38 0.37 0.21

coefficient,%import%penetration%on%domestic%HH

0.41 0.65 0.24 0.47

coefficient,%share%imports%on%share%sales index%import%share%dispersion index%intraindustry%trade

Import,dispersion,statistics

fraction%sales%by%top%0.01%producers fraction%sales%by%top%0.05%producers fraction%wages%top%0.01%producers fraction%wages%top%0.05%producers

Size,distribution,producers,,domestic,sales

fraction%sales%by%top%0.01%sectors fraction%sales%by%top%0.05%sectors fraction%wages%(same)%top%0.01%sectors fraction%wages%(same)%top%0.05%sectors

0.26 0.52 0.11 0.32

0.16 0.41 0.92

p10%top%share p50%top%share p90%top%share

Size,distribution,sectors,,domestic,sales

1.17 3.73 13.82

0.04 0.005 0.02 0.19 0.59 0.11

7.25 3.92 0.45 0.40

p10%inverse%HH p50%inverse%HH p90%inverse%HH

Across§or,concentration

mean%share median%share p75%share p95%share p99%share std%dev%share

Distribution,of,sectoral,shares,,domestic,sales

mean%inverse%HH median%inverse%HH mean%top%share median%top%share

Within§or,concentration,,domestic,sales

Data

FF

FF FF FF

0.34 0.58 0.32 0.54

0.24 0.35 0.25 0.36

3 16 51

0.20 0.33 0.60

2.00 4.88 9.97

0.04 0.007 0.04 0.23 0.49 0.10

5.53 4.88 0.38 0.33

Autarky

0.08

0.77 0.23 0.50

0.37 0.64 0.35 0.60

0.24 0.36 0.25 0.37

3 16 47

0.23 0.41 0.74

1.74 3.82 7.94

0.05 0.006 0.03 0.27 0.59 0.11

4.43 3.82 0.45 0.41

0.21

0.79 0.23 0.51

0.37 0.63 0.35 0.59

0.24 0.36 0.25 0.37

3 15 47

0.23 0.40 0.78

1.58 3.90 8.27

0.05 0.006 0.03 0.26 0.60 0.12

4.52 3.90 0.45 0.40

Benchmark Alternative

0.12

0.72 0.26 0.47

0.33 0.61 0.22 0.50

0.21 0.33 0.14 0.23

3 16 48

0.22 0.40 0.72

1.78 3.93 8.39

0.05 0.006 0.03 0.26 0.57 0.11

4.64 3.93 0.44 0.40

Labor5wedges

0.09

0.82 0.24 0.49

0.37 0.64 0.34 0.60

0.24 0.36 0.25 0.37

3 16 47

0.23 0.41 0.74

1.74 3.80 7.96

0.05 0.006 0.03 0.27 0.59 0.11

4.45 3.80 0.45 0.41

Tariffs

F0.17

0.56 0.42 0.30

0.42 0.74 0.40 0.72

0.25 0.38 0.26 0.38

3 13 39

0.30 0.57 0.93

1.16 2.41 5.48

0.06 0.004 0.03 0.37 0.85 0.15

3.00 2.41 0.59 0.57

Bertrand

F0.08

0.97 0.14 0.61

0.42 0.66 0.38 0.60

0.29 0.41 0.29 0.41

3 15 47

0.21 0.40 0.74

1.73 4.07 9.12

0.05 0.009 0.03 0.25 0.59 0.11

4.93 4.07 0.44 0.40

Low5γ

Table5A4:5Moments5implied5by5Robustness5Experiments

0.53

0.33 0.46 0.28

0.40 0.70 0.38 0.67

0.25 0.37 0.26 0.38

3 13 32

0.24 0.44 0.84

1.39 3.27 6.79

0.04 0.004 0.02 0.26 0.65 0.12

11.85 3.27 0.49 0.44

High5γ

0.06

0.77 0.21 0.52

0.85 1.00 0.83 1.00

0.25 0.37 0.25 0.37

2 15 52

0.21 0.40 0.72

1.78 3.94 9.10

0.00 0.000 0.00 0.00 0.09 0.03

15.56 3.94 0.43 0.40

0.06

0.72 0.21 0.51

0.38 0.64 0.35 0.60

0.23 0.35 0.23 0.35

3 15 48

0.23 0.41 0.73

1.77 3.86 7.84

0.05 0.005 0.03 0.26 0.58 0.11

4.40 3.86 0.45 0.41

0.69

F0.01 0.56 0.25

0.34 0.60 0.31 0.56

0.21 0.33 0.21 0.33

3 17 51

0.21 0.40 0.78

1.57 4.09 8.54

0.05 0.006 0.03 0.24 0.58 0.11

4.70 4.09 0.45 0.40

No5fix5costs Gauss.5copula n(s),n*(s)

0.32

0.81 0.28 0.41

0.41 0.65 0.24 0.47

0.24 0.51 0.11 0.32

5 36 138

0.14 0.30 0.63

2.14 6.09 16.38

0.01 0.002 0.01 0.06 0.22 0.05

14.97 7.98 0.30 0.25

Data

0.21

0.47 0.33 0.41

0.46 0.74 0.42 0.71

0.24 0.36 0.23 0.35

6 36 127

0.17 0.33 0.60

2.43 5.74 12.90

0.02 0.003 0.01 0.09 0.32 0.06

13.32 5.74 0.36 0.33

5Hdigit Model

p75/p50 p90/p50 p95/p50 p99/p50

Across3sector*markup*distribution

p75/p50 p90/p50 p95/p50 p99/p50

Unconditional*markup*distribution

1.099 1.314 1.562 2.579

1.009 1.036 1.081 1.333

00

1.35

average/aggregate$labor$share

aggregate$markup

4.00 0.38 0.25

00 00 00 00

trade$elasticity import$share fraction$exporters

TFP$loss$autarky,$% TFP$loss$Taiwan,$% gains$from$trade,$% pro0competitive$gains,

%$Data

1.087 1.232 1.387 1.646

1.019 1.085 1.174 1.497

1.31

1.16

4.00 0.38 0.25

8.5 6.7 12.0 1.8

Benchmark

1.085 1.234 1.400 1.522

1.020 1.087 1.167 1.474

1.30

1.14

4.00 0.38 0.25

8.4 5.7 11.7 2.6

Alternative

1.082 1.212 1.395 1.606

1.018 1.081 1.166 1.473

1.30

1.35

4.00 0.38 0.25

8.2 6.4 12.1 1.8

Labor3wedges

1.089 1.233 1.393 1.639

1.047 1.129 1.215 1.532

1.34

1.16

4.00 0.38 0.25

8.5 6.7 14.0 3.9

Tariffs

1.055 1.177 1.299 1.754

1.001 1.012 1.038 1.195

1.21

1.08

4.00 0.38 0.25

3.9 1.9 13.1 2.0

Bertrand

1.113 1.342 1.497 2.039

1.019 1.085 1.185 1.630

1.54

1.20

2.38 0.38 0.25

9.1 6.8 19.0 2.3

Low3γ

1.076 1.225 1.340 1.555

1.009 1.079 1.164 1.445

1.26

1.17

4.00 0.38 0.25

9.0 8.2 11.1 0.8

High3γ

1.085 1.224 1.397 1.634

1.000 1.000 1.002 1.028

1.31

1.18

4.00 0.38 1.00

8.5 6.7 11.5 1.8

1.088 1.249 1.396 1.578

1.018 1.084 1.166 1.486

1.33

1.17

4.00 0.38 0.25

8.6 7.1 11.5 1.5

No3fix3costs Gauss.3copula

Table3A5:3Gains3from3Trade3and3Markup3Distributions3implied3by3Robustness3Experiments

1.065 1.181 1.299 1.643

1.014 1.069 1.142 1.394

1.30

1.15

2.47 0.38 0.25

8.2 6.4 49.4 1.8

n(s),n*(s)

1.053 1.156 1.273 1.525

1.003 1.021 1.054 1.211

1.27

1.13

4.00 0.38 0.25

6.1 5.8 12.1 0.3

5Fdigit

Table/A6:/Fixed/N/Experiments Panel/A:/No/Idiosyncratic/Productivity/Draws Autarky

Free/trade τ(ρ)=1 τ(ρ)=0

N=1

Autarky

Free/trade τ(ρ)=1 τ(ρ)=0

N=2 TFP$loss,$% import$share fraction$exporters trade$elasticity

0 0 0 ??

0 0.5 1 1.39

13.2 0.5 1 0.61

TFP$loss,$% import$share fraction$exporters trade$elasticity

0 0 0 ??

0 0.5 1 2.90

0.8 0.5 1 0.67

aggregate$markup domestic$markup import$markup

4.57 4.57 ??

1.79 1.79 1.79

3.25 3.25 3.25

aggregate$markup domestic$markup import$markup

1.79 1.79 ??

1.37 1.37 1.37

1.73 1.73 1.73

Markup'dispersion unconditional sectoral

0 0

0 0

0.94 0

Markup'dispersion unconditional sectoral

0 0

0 0

0.27 0

TFP$loss,$% import$share fraction$exporters trade$elasticity

0 0 0 ??

0 0.5 1 6.65

0.02 0.5 1 0.76

TFP$loss,$% import$share fraction$exporters trade$elasticity

0 0 0 ??

0 0.5 1 7.67

0.004 0.5 1 0.70

aggregate$markup domestic$markup import$markup

1.20 1.20 ??

1.16 1.16 1.16

1.20 1.20 1.20

aggregate$markup domestic$markup import$markup

1.16 1.16 ??

1.13 1.13 1.13

1.15 1.15 1.15

Markup'dispersion unconditional sectoral

0 0

0 0

0.04 0

Markup'dispersion unconditional sectoral

0 0

0 0

0.01 0

N=10

N=20

Panel/B:/With/Idiosyncratic/Productivity/Draws Autarky

Free/trade τ(ρ)=1 τ(ρ)=0

N=1

Autarky

Free/trade τ(ρ)=1 τ(ρ)=0

N=2 TFP$loss,$% import$share fraction$exporters trade$elasticity

0 0 0 ??

4.3 0.5 1 1.35

12.6 0.5 1 0.58

TFP$loss,$% import$share fraction$exporters trade$elasticity

3.9 0 0 ??

6.3 0.5 1 2.63

5.7 0.5 1 0.66

aggregate$markup domestic$markup import$markup

4.57 4.57 ??

1.84 1.84 1.84

3.33 3.33 3.33

aggregate$markup domestic$markup import$markup

1.83 1.83 ??

1.47 1.47 1.47

1.78 1.78 1.78

Markup'dispersion unconditional sectoral

0 0

0.24 0

0.94 0

Markup'dispersion unconditional sectoral

0.23 0.11

0.29 0.18

0.42 0.10

TFP$loss,$% import$share fraction$exporters trade$elasticity

7.5 0 0 ??

7.3 0.5 1 4.26

7.8 0.5 1 0.70

TFP$loss,$% import$share fraction$exporters trade$elasticity

6.7 0 0 ??

6.4 0.5 1 4.65

10.6 0.5 1 0.69

aggregate$markup domestic$markup import$markup

1.34 1.34 ??

1.31 1.31 1.31

1.35 1.35 1.35

aggregate$markup domestic$markup import$markup

1.29 1.29 ??

1.28 1.28 1.28

1.36 1.36 1.36

Markup'dispersion unconditional sectoral

0.24 0.22

0.17 0.23

0.19 0.25

Markup'dispersion unconditional sectoral

0.17 0.25

0.10 0.27

0.11 0.26

N=10

N=20

Notes: N$is$number$of$producers$per$sector$per$country No$fixed$cost$of$operating,$f_d$=$0 Free$trade$means τ =$1$(no$net$trade$cost)$and$f_x$=$0 Markup$dispersion$is$measured$as$log$of$p99/p50$ratio

Other'parameters: α output'elasticity'of'capital β time'discount'factor δ capital'depreciation'rate 0.33 0.96 0.1

2.0

0

pro6competitive'gains,'%

16.3

14.3

change'welfare,'% (including'transition)

18.0 18.0 18.0 0

15.3 15.3 15.3 0

change'C,'% change'K,'% change'Y,'% change'L,'%

12.0 0

10.2 0

change'TFP,'% change'markup,'%

Standard*model

Aggregate markup*constant

3.1

17.4

18.7 22.1 19.3 0

12.0 62.8

0

3.3

17.6

19.9 23.2 20.5 1.1

12.0 62.8

3.3

17.6

20.4 23.8 21.0 1.7

12.0 62.8

3.3

17.6

21.0 24.4 21.6 2.3

12.0 62.8

Frisch*elasticity*of*labor*supply*(1/η) 0.5 1 2

Variable*markups

Table*A7:*Gains*from*Trade*with*Capital*Accumulation*and*Elastic*Labor*Supply

3.9

18.2

22.1 25.5 22.7 3.4

12.0 62.8

Inf

γ θ ξ_x ξ_z ζ f_d f_x τ τ(ρ)

within§or,elasticity,of,substitution across§or,elasticity,of,substitution pareto,shape,parameter,,idiosyncratic,productivity pareto,shape,parameter,,sector,productivity geometric,parameter,,number,producers,per,sector fixed,cost,of,domestic,operations fixed,cost,of,export,operations trade,cost kendall's,tau,for,gumbel,copula,

10 1.28 4.53 0.56 0.043 0.0043 0.211 1.128 0.93

Benchmark 10 1.28 4.53 0.56 0.043 0.0043 0.211 1.245 0.93

10 1.28 4.53 0.56 0.043 0.0043 0.211 1.510 0.94

Larger4trading4partner L*=2L L*=10L

Table4A8:4Parameters4for4Asymmetric4Countries4Experiments

10 1.28 4.53 0.56 0.043 0.0043 0.231 1.322 0.83

10 1.28 4.53 0.56 0.043 0.0043 0.350 2.624 0.55

More4productive4trading4partner Abar*=2Abar Abar*=10Abar

2 10 52

p10%number%producers p50%number%producers p90%number%producers

coefficient,%share%imports%on%share%sales index%import%share%dispersion index%intraindustry%trade

Import,dispersion,statistics

fraction%sales%by%top%0.01%producers fraction%sales%by%top%0.05%producers fraction%wages%top%0.01%producers fraction%wages%top%0.05%producers

Size,distribution,producers,,domestic,sales

fraction%sales%by%top%0.01%sectors fraction%sales%by%top%0.05%sectors fraction%wages%(same)%top%0.01%sectors fraction%wages%(same)%top%0.05%sectors

0.81 0.38 0.37

0.41 0.65 0.24 0.47

0.26 0.52 0.11 0.32

0.16 0.41 0.92

p10%top%share p50%top%share p90%top%share

Size,distribution,sectors,,domestic,sales

1.17 3.73 13.82

0.04 0.005 0.02 0.19 0.59 0.11

7.25 3.92 0.45 0.40

p10%inverse%HH p50%inverse%HH p90%inverse%HH

Across§or,concentration

mean%share median%share p75%share p95%share p99%share std%dev%share

Distribution,of,sectoral,shares,,domestic,sales

mean%inverse%HH median%inverse%HH mean%top%share median%top%share

Within§or,concentration,,domestic,sales

Data

0.77 0.23 0.50

0.37 0.64 0.35 0.60

0.24 0.36 0.25 0.37

3 16 47

0.23 0.41 0.74

1.74 3.82 7.94

0.05 0.006 0.03 0.27 0.59 0.11

4.43 3.82 0.45 0.41

0.77 0.23 0.50

0.37 0.64 0.35 0.60

0.24 0.36 0.25 0.37

3 16 47

0.23 0.41 0.74

1.74 3.82 7.94

0.05 0.006 0.03 0.27 0.59 0.11

4.43 3.82 0.45 0.41

Benchmark Home( Foreign

0.80 0.22 0.44

0.37 0.64 0.35 0.60

0.24 0.36 0.25 0.37

3 16 47

0.23 0.41 0.73

1.76 3.81 7.95

0.05 0.006 0.03 0.27 0.59 0.11

4.42 3.81 0.45 0.41

0.68 0.21 0.44

0.36 0.62 0.34 0.58

0.25 0.36 0.25 0.37

3 16 51

0.21 0.37 0.67

1.95 4.33 8.78

0.04 0.006 0.03 0.24 0.52 0.10

4.94 4.33 0.42 0.37

0.83 0.20 0.20

0.37 0.64 0.35 0.60

0.24 0.36 0.25 0.37

3 16 47

0.23 0.41 0.73

1.75 3.85 7.96

0.05 0.006 0.03 0.27 0.59 0.11

4.43 3.85 0.45 0.41

0.27 0.19 0.19

0.40 0.69 0.37 0.64

0.24 0.35 0.25 0.36

3 18 63

0.18 0.33 0.61

2.00 4.90 10.92

0.03 0.004 0.01 0.17 0.42 0.08

16.50 4.90 0.37 0.34

Larger(trading(partner L*=2L L*=10L Home( Foreign Home( Foreign

Table(A9:(Moments(implied(by(Asymmetric(Countries(Experiments

0.48 0.41 0.27

0.36 0.63 0.34 0.59

0.24 0.37 0.25 0.38

3 14 46

0.23 0.42 0.79

1.56 3.73 7.95

0.05 0.006 0.03 0.27 0.62 0.12

4.41 3.73 0.46 0.42

0.56 0.38 0.26

0.36 0.62 0.34 0.58

0.25 0.36 0.26 0.37

3 16 51

0.21 0.38 0.69

1.88 4.25 8.88

0.05 0.006 0.03 0.24 0.53 0.11

4.92 4.24 0.42 0.38

0.11 0.72 0.05

0.33 0.60 0.31 0.55

0.22 0.36 0.23 0.36

2 13 46

0.22 0.41 0.86

1.34 3.82 8.34

0.05 0.007 0.04 0.27 0.65 0.12

4.53 3.82 0.47 0.41

F0.02 0.76 0.04

0.40 0.68 0.36 0.64

0.23 0.35 0.24 0.36

3 17 62

0.18 0.34 0.65

1.98 4.80 11.11

0.03 0.004 0.01 0.17 0.44 0.08

16.49 4.81 0.38 0.34

More(productive(trading(partner Abar*=2Abar Abar*=10Abar Home( Foreign Home( Foreign

1 1.16 1.31

relative$real$wage average/aggregate$labor$share aggregate$markup

p75/p50 p90/p50 p95/p50 p99/p50

Across3sector*markup*distribution

p75/p50 p90/p50 p95/p50 p99/p50

1.087 1.232 1.387 1.646

1.019 1.085 1.174 1.497

4.00 0.38 0.25

trade$elasticity import$share fraction$exporters

Unconditional*markup*distribution

8.5 6.7 12.0 1.8

TFP$loss$autarky,$% TFP$loss,$% gains$from$trade,$% proCcompetitive$gains,

%$1.087 1.232 1.387 1.646

1.019 1.085 1.174 1.497

1 1.16 1.31

4.00 0.38 0.25

8.5 6.7 12.0 1.8

Benchmark Home% Foreign

1.082 1.232 1.374 1.623

1.019 1.085 1.173 1.497

1 1.16 1.31

4.00 0.38 0.25

8.5 7.0 11.7 1.5

1.089 1.245 1.434 1.667

1.017 1.083 1.175 1.483

0.94 1.16 1.32

4.14 0.21 0.18

8.5 6.9 6.1 1.7

1.094 1.261 1.349 1.629

1.019 1.084 1.173 1.495

1 1.16 1.31

4.00 0.38 0.25

8.5 7.3 11.1 1.2

1.104 1.289 1.594 1.969

1.007 1.056 1.133 1.429

0.89 1.18 1.34

4.26 0.05 0.06

8.5 7.4 1.8 1.1

Larger%trading%partner L*=2L L*=10L Home% Foreign Home% Foreign

1.157 1.446 1.518 1.821

1.020 1.090 1.181 1.512

1 1.16 1.32

4.00 0.38 0.25

8.5 7.3 15.0 1.3

1.100 1.233 1.347 1.722

1.018 1.087 1.179 1.505

1.85 1.16 1.33

3.15 0.21 0.16

8.5 7.0 7.9 1.5

2.123 3.775 4.147 5.191

1.009 1.059 1.136 1.443

1 1.17 1.34

4.00 0.38 0.25

8.7 7.5 30.1 1.2

1.096 1.258 1.423 3.081

1.007 1.057 1.136 1.451

7.8 1.19 1.35

1.27 0.07 0.03

8.7 8.4 6.2 0.3

More%productive%trading%partner Abar*=2Abar Abar*=10Abar Home% Foreign Home% Foreign

Table%A10:%Gains%from%Trade%and%Markup%Distributions%implied%by%Asymmetric%Countries%Experiments

0.097 && && 0.173 && &&

probability,of,successful,entry

probability,of,high&productivity,draw,given,success

fraction,of,sectors,of,type,(Omega1,Omega2)

Ω_1(1) Ω_1(2) Ω_1(3)

Ω_2(1) Ω_2(2) Ω_2(3)

ν(1) ν(2) ν(3) ν(4) ν(5) ν(6) ν(7) ν(8) ν(9)

1 && && && && && && && &&

1.915 0.233 0.147 1.134

10 1.28

high,productivity,draw entry,cost std,dev,of,log,productivity,gap,,domestic,vs.,export trade,cost

within§or,elasticity,of,substitution across§or,elasticity,of,substitution

xbar f_e σ_u τ

Calibrated

γ θ

Common%to%all%free,entry%experiments

Free#entry

A:#One#type#of#sector

0.138 0.111 0.146 0.154 0.205 0.029 0.096 0.060 0.061

0.180 0.441 0.421

0.047 0.041 0.293

1.915 0.220 0.175 1.131

10 1.28

Free#entry

Table#A11:#Parameters#for#Free#Entry#Experiments

0.192 0.119 0.043 0.101 0.087 0.103 0.116 0.141 0.100

0.064 0.098 0.052

0.109 0.013 0.301

1.748 0.198 0.171 1.127

10 1.28

15%

0.097 0.062 0.087 0.118 0.131 0.140 0.087 0.148 0.130

0.154 0.059 0.260

0.158 0.012 0.313

1.718 0.185 0.174 1.136

10 1.28

0.123 0.112 0.086 0.134 0.099 0.099 0.111 0.145 0.091

0.198 0.212 0.170

0.078 0.015 0.281

1.638 0.186 0.135 1.126

10 1.28

Free#entry#with#collusion 25% 35%

B:#Nine#types#of#sectors

0.108 0.188 0.036 0.051 0.067 0.173 0.203 0.165 0.009

0.327 0.126 0.203

0.122 0.028 0.339

1.487 0.258 0.196 1.133

10 1.28

50%

2 10 52

p10%number%producers p50%number%producers p90%number%producers

fraction%sales%by%top%0.01%producers fraction%sales%by%top%0.05%producers fraction%wages%top%0.01%producers fraction%wages%top%0.05%producers

Size,distribution,producers,,domestic,sales

fraction%sales%by%top%0.01%sectors fraction%sales%by%top%0.05%sectors fraction%wages%(same)%top%0.01%sectors fraction%wages%(same)%top%0.05%sectors

0.41 0.65 0.24 0.47

0.26 0.52 0.11 0.32

0.16 0.41 0.92

p10%top%share p50%top%share p90%top%share

Size,distribution,sectors,,domestic,sales

1.17 3.73 13.82

0.04 0.005 0.02 0.19 0.59 0.11

7.25 3.92 0.45 0.40

p10%inverse%HH p50%inverse%HH p90%inverse%HH

Across§or,concentration

mean%share median%share p75%share p95%share p99%share std%dev%share

Distribution,of,sectoral,shares,,domestic,sales

mean%inverse%HH median%inverse%HH mean%top%share median%top%share

Within§or,concentration,,domestic,sales

Data

0.36 0.62 0.34 0.59

0.24 0.36 0.25 0.37

3 15 47

0.23 0.40 0.82

1.45 3.88 8.11

0.05 0.006 0.03 0.27 0.62 0.12

4.47 3.88 0.46 0.40

No#entry (benchmark)

0.35 0.61 0.34 0.60

0.19 0.38 0.19 0.38

13 18 23

0.16 0.30 0.58

2.32 3.72 6.30

0.06 0.007 0.02 0.31 0.46 0.11

4.35 3.72 0.33 0.30

Free#entry

A:#One#type#of#sector

0.34 0.60 0.33 0.58

0.20 0.38 0.20 0.38

5 9 51

0.08 0.25 0.65

2.05 4.10 13.41

0.06 0.002 0.05 0.25 0.49 0.11

6.25 4.10 0.31 0.25

Free#entry

Table#A12:#Moments#implied#by#Free#Entry#Experiments

0.33 0.59 0.31 0.56

0.19 0.38 0.19 0.38

2 15 44

0.07 0.33 0.67

2.00 4.10 15.00

0.06 0.014 0.04 0.31 0.51 0.12

6.20 4.10 0.37 0.33

15%

0.36 0.63 0.34 0.60

0.19 0.38 0.20 0.38

2 27 54

0.07 0.24 0.69

1.98 5.72 16.83

0.04 0.007 0.02 0.19 0.50 0.09

7.87 5.72 0.31 0.24

0.33 0.58 0.31 0.56

0.19 0.38 0.19 0.38

3 13 51

0.08 0.25 0.64

2.00 4.69 15.00

0.05 0.010 0.05 0.25 0.50 0.10

7.17 4.69 0.31 0.25

Free#entry#with#collusion 25% 35%

B:#Nine#types#of#sectors

0.28 0.51 0.27 0.49

0.19 0.38 0.19 0.38

3 12 39

0.09 0.25 0.57

2.08 5.56 15.81

0.07 0.021 0.07 0.26 0.50 0.10

7.68 5.56 0.30 0.25

50%

p75/p50 p90/p50 p95/p50 p99/p50

1.03 1.05 1.06 1.08

191 176

measure$of$entrants$N,$autarky measure$of$entrants$N,$Taiwan

1.10 1.31 1.56 2.58

6.4 1.5

gains$from$trade,$% proDcompetitive$gains,

%$Across§or)markup)distribution

3.2 1.7

TFP$loss$autarky,$% TFP$loss$Taiwan,

%$Data

Free#entry

A:#One#type#of#sector

1.08 1.14 1.16 1.22

187 168

7.2 1.2

3.2 2.0

Free#entry

1.05 1.16 1.25 1.47

137 140

12.1 3.8

8.3 4.5

15%

1.09 1.22 1.30 1.52

162 160

11.2 3.9

8.1 4.3

1.06 1.14 1.20 1.36

177 171

10.0 3.3

7.9 4.6

Free#entry#with#collusion 25% 35%

B:#Nine#types#of#sectors

Table#A13:#Gains#from#Trade#and#Markup#Distributions#implied#by#Free#Entry#Experiments

1.06 1.13 1.19 1.34

114 110

9.7 2.6

7.1 4.6

50%